1. The Field of the Invention
This invention relates to digital image processing. More specifically, it relates to a method and system for setting image analysis parameters to control image analysis operations.
2. The Relevant Technology
Innovations in automated screening systems for biological and other research are capable of generating enormous amounts of data. The massive volumes of feature-rich data being generated by these systems and the effective management and use of information from the data has created a number of very challenging problems. As is known in the art, “feature-rich” data includes data wherein one or more individual features of an object of interest (e.g., a cell) can be collected. To fully exploit the potential of data from high-volume data generating screening instrumentation, there is a need for new methods to optimally set the image analysis parameters that control the image analysis operations that generate this feature rich data.
Identification, selection, validation and screening of new drug compounds or cures for disease is often completed at a nucleotide level using sequences of Deoxyribonucleic Acid (“DNA”), Ribonucleic Acid (“RNA”) or other nucleotides. “Genes” are regions of DNA, and “proteins” are the products of genes. The existence and concentration of protein molecules typically help determine if a gene is “expressed” or “repressed” in a given situation. Responses of genes to natural and artificial compounds are typically used to improve existing drugs, and develop new drugs. However, it is often more appropriate to determine the effect of a new compound on a cellular level instead of a nucleotide level.
Cells are the basic units of life and integrate information from DNA, RNA, proteins, metabolites, ions and other cellular components. New compounds that may look promising at a nucleotide level may be toxic at a cellular level. Florescence-based reagents can be applied to cells to determine ion concentrations, membrane potentials, enzyme activities, gene expression, as well as the presence of metabolites, proteins, lipids, carbohydrates, and other cellular components.
There are two types of cell screening methods that are typically used: (1) fixed cell screening; and (2) live cell screening. For fixed cell screening, initially living cells are treated with experimental compounds being tested. No environmental control of the cells is provided after application of a desired compound and the cells may die during screening. Live cell screening requires environmental control of the cells (e.g., temperature, humidity, gases, etc.) after application of a desired compound, and the cells are kept alive during screening. Fixed cell assays allow spatial measurements to be obtained, but only at one point in time. Live cell assays allow both spatial and temporal measurements to be obtained.
The spatial and temporal frequency of chemical and molecular information present within cells makes it possible to extract feature-rich cell information from populations of cells. For example, multiple molecular and biochemical interactions, cell kinetics, changes in sub-cellular distributions, changes in cellular morphology, changes in individual cell subtypes in mixed populations, changes and sub-cellular molecular activity, changes in cell communication, and other types of cell information can be obtained.
The types of biochemical and molecular cell-based assays now accessible through fluorescence-based reagents is expanding rapidly. The need for automatically extracting additional information from a growing list of cell-based assays has allowed automated platforms for feature-rich assay screening of cells to be developed. For example, the ArrayScan System and the KineticScan System by Cellomics, Inc. of Pittsburgh, Pa., are such feature-rich cell screening systems. Cell based imaging systems such as Discovery 1, ImageExpress, and FLIPR, by Molecular Devices, Inc. of Sunnyvale, Calif., IN Cell Analyzer 1000 and IN Cell Analyzer 1000, by General Electric Healthcare of Little Chalfont, United Kingdom, Pathway HT, by BD Biosciences or Rockville, Md., and others also generate large amounts of data and photographic images that would benefit from efficient data management solutions. Photographic images are typically collected using a digital camera. Collecting and storing a large number of photographic images adds to the data problems encountered when using high throughput systems. For more information on fluorescence based systems, see “Bright ideas for high-throughput screening—One-step fluorescence HTS assays are getting faster, cheaper, smaller and more sensitive,” by Randy Wedin, Modern Drug Discovery, Vol. 2(3), pp. 61-71, May/June 1999.
Such automated feature-rich cell screening systems and other systems known in the art typically include microplate scanning hardware, fluorescence excitation of cells, fluorescence captive emission optics, a photographic microscope with a camera, data collection, data storage and data display capabilities. For more information on feature-rich cell screening see “High content fluorescence-based screening,” by Kenneth A. Guiliano, et al., Journal of Biomolecular Screening, Vol. 2, No. 4, pp. 249-259, Winter 1997, ISSN 1087-0571, “PTH receptor internalization,” Bruce R. Conway, et al., Journal of Biomolecular Screening, Vol. 4, No. 2, pp. 75-68, April 1999, ISSN 1087-0571, “Fluorescent-protein biosensors: new tools for drug discovery,” Kenneth A. Giuliano and D. Lansing Taylor, Trends in Biotechnology, (“TIBTECH”), Vol. 16, No. 3, pp. 99-146, March 1998, ISSN 0167-7799, all of which are incorporated by reference.
An automated feature-rich cell screening system typically automatically scans a microplate plate with multiple wells and acquires multi-color fluorescence data of cells at one or more instances of time at a pre-determined spatial resolution. Automated feature-rich cell screen systems typically support multiple channels of fluorescence to collect multi-color fluorescence data at different wavelengths and may also provide the ability to collect cell feature information on a cell-by-cell basis including such features as the size and shape of cells and sub-cellular measurements of organelles within a cell.
The need for optimal setting of image analysis parameters is not limited to feature-rich cell screening systems or to cell based arrays. Virtually any instrument that runs High Throughput Screening (“HTS”) assays also utilize optimal parameter settings to generate data from analysis routines.
In feature-rich cell screening systems, image analysis methods are typically controlled by a set of input parameters that allow the user to adjust them to perform in a given environment. Automated high content cell-based screening methods and software have input parameters that control how they process the fluorescence images collected by cell screening instruments. These parameters must be adjusted to match the biological cell type, the biological target within a cell, and the imaging conditions used in a particular assay.
Considerable effort is expended in identifying optimal image analysis parameters to produce the ideal protocols to accompany validated high content assay method and software products. Likewise, end users of such high content assay method and software products expend considerable effort in tuning image analysis protocol parameters to suit their biology and have identified this process as a usability issue.
Current methods of setting image analysis parameters to control image analysis operations consist of: (1) Manually creating analysis protocols with different processing parameters—using expert domain knowledge—and storing them in a file or database; (2) Running the application using each analysis protocol on stored image data or in real-time; (3) Exporting the resulting plate data for analysis and comparison using expert domain knowledge; and repeating these three steps until the results are satisfactory.
However, such methods may not provide satisfactory results. Thus, it is desirable provide methods of digital image analysis that are faster, far less tedious, and mitigate the need for detailed knowledge of the underlying image.